Student Next Assignment Submission Prediction Using a Machine Learning Approach

Salal, Y K; Hussain, Mushtaq; Theodorou, Paraskevi

doi:10.1007/978-3-030-71119-1_38

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…Another more comprehensive study by Y.K. Salal, M. Hussain, and T. Paraskevi focuses on using machine learning techniques including Support Vector Machines (SVM), Random Forest (RF), and Naive Bayes (NB) to predict the next assignment submission of a student and managed to achieve an accuracy of 85.5% [13]. Although the study was conducted dataset of relatively small size, the results implicate its effective extension to larger datasets as well.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Student Grade Prediction Using Hybrid Stacking Machine Learning Model

Reddy,

Pande

2024

EAI Endorsed Trans IoT

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With increasing technical procedures, academic institutions are adapting to a data-driven decision-making approach of which grade prediction is an integral part. The purpose of this study is to propose a hybrid model based on a stacking approach and compare its accuracy with those of the individual base models. The model hybridizes K-nearest neighbours, Random forests, XGBoost and multi-layer perceptron networks to improve the accuracy of grade prediction by enabling a combination of strengths of different algorithms for the creation of a more robust and accurate model. The proposed model achieved an average overall accuracy of around 90.9% for 10 epochs, which is significantly higher than that achieved by any of the individual algorithms of the stack. The results demonstrate the improvement of prediction results but using a stacking approach. This study has significant implications for academic institutions which can help them make informed grade predictions for the improvement of student outcomes.

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Section: Literature Reviewmentioning

confidence: 99%

Improving Student Grade Prediction Using Hybrid Stacking Machine Learning Model

Reddy,

Pande

2024

EAI Endorsed Trans IoT

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“…While primarily used for binary classification, logistic regression can be extended to handle multinomial outcomes (multiple categories) through variations like multinomial logistic regression. Logistic regression models are widely used in various fields, such as biology and social sciences, where the objective is to predict a categorical outcome [29].…”

Section: B Logistic Regression Classifiermentioning

confidence: 99%

Comparative Evaluation Of Machine Learning Classifiers For Brain Tumor Detection

Ali

2024

Preprint

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Brain tumors, which are abnormal growths of cells in the brain, represent a significant health concern, necessitating prompt and accurate detection for effective treatment. If left untreated, brain tumors can lead to severe complications, including cognitive impairment, paralysis, and even death. This study evaluates six machine learning classifiers: Support Vector Classifier (SVC), Logistic Regression Classifier, K-Nearest Neighbors (KNN) Classifier, Naive Bayes Classifier, Decision Tree Classifier, and Random Forest Classifier - on a comprehensive brain tumor dataset. Our results showed that Random Forest achieved the highest accuracy of 98.27%, demonstrating its potential in detecting brain tumors. However, Support Vector Classifier (SVC) emerged as the top performer, achieving an impressive accuracy of 97.74%, showcasing its exceptional ability to detect brain tumors accurately. This significant improvement in SVC’s performance highlights its potential as a reliable tool for medical diagnostics, contributing to the development of efficient and accurate automated systems for early brain tumor diagnosis, ultimately aiming to improve patient outcomes and treatment efficacy.

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“…In [32][33][34][35], real datasets were used to identify students at risk of failure at an early stage of a semester. In [32], the authors built a model using a deep dense neural network and some traditional ML techniques such as decision tree, Knearest neighbor, random forest, and naïve Bayes, and it showed that AUC and F1-score metrics are better than accuracy in the case of the imbalanced dataset.…”

Section: Related Workmentioning

confidence: 99%

“…The results achieved using all parameters were better than those obtained using only academic parameters. In [35], the authors built a model to predict students' performance in the next assignment submission based on various features such as final result, number of previous attempts, student credit, total clicks, student ID, age, and gender. Random forest, logistic regression, and Knearest neighbor were applied in this study.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks

Nabil¹,

Seyam

AbouElfetouh

2021

IEEE Access

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Predicting students' academic performance at an early stage of a semester is one of the most crucial research topics in the field of Educational Data Mining (EDM). Students are facing various difficulties in courses like "Programming" and "Data Structures" through undergraduate programs, which is why failure and dropout rates in these courses are high. Therefore, EDM is used to analyze students' data gathered from various educational settings to predict students' academic performance, which would help them to achieve better results in their future courses. The main goal of this paper is to explore the efficiency of deep learning in the field of EDM, especially in predicting students' academic performance, to identify students at risk of failure. A dataset collected from a public 4-year university was used in this study to develop predictive models to predict students' academic performance of upcoming courses given their grades in the previous courses of the first academic year using a deep neural network (DNN), decision tree, logistic regression, support vector classifier, and K-nearest neighbor. In addition, we made a comparison between various resampling methods to solve the imbalanced dataset problem, such as SMOTE, ADASYN, ROS, and SMOTE-ENN. From the experimental results, it is observed that the proposed DNN model can predict students' performance in a data structure course and can also identify students at risk of failure at an early stage of a semester with an accuracy of 89%, which is higher than models like decision tree, logistic regression, support vector classifier, and Knearest neighbor.INDEX TERMS Deep neural networks, educational data mining, imbalanced dataset problem, machine learning, predicting students' performance, resampling methods.

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Student Next Assignment Submission Prediction Using a Machine Learning Approach

Cited by 5 publications

References 10 publications

Improving Student Grade Prediction Using Hybrid Stacking Machine Learning Model

Improving Student Grade Prediction Using Hybrid Stacking Machine Learning Model

Comparative Evaluation Of Machine Learning Classifiers For Brain Tumor Detection

Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks

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